{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.insert(0, \"../../\")\n",
    "#  演示必要的准备代码，使用该库时不需重复此单元格命令"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xalpha as xa\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文将分析大陆公募基金市场中和石油商品相关的基金，考察其相关性和可替代性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "hb = xa.fundinfo(\"162411\")  # 华宝油气\n",
    "gf = xa.fundinfo(\"162719\")  # 广发石油\n",
    "ha = xa.fundinfo(\"160416\")  # 华安石油\n",
    "nf = xa.fundinfo(\"501018\")  # 南方原油\n",
    "yfd = xa.fundinfo(\"161129\")  # 易方达原油\n",
    "js = xa.fundinfo(\"160723\")  # 嘉实原油"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "oilcomp = xa.evaluate(hb, gf, ha, nf, yfd, js)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "观察上述的相关系数图，可以看到高关联的基金自动分成两个区。三个跟踪原油期货的基金关联极高。同时三个在美股为主跟踪石油产业企业股票指数的基金（对应指数不同）关联极高。另一方面，两者之间关联并没有特别高。像经常宣传和原油价格走势关联很高的华宝油气，其关联系数也只有 0.67 左右，并不是很高。这种跟踪意义不大，下面不同基金的净值图将这个问题表现的更明显。\n",
    "如果非想持有石油股票指数来减少原油期货换仓损失的话，华安石油相比华宝油气具有更高的关联性，相关系数在 0.73 左右，高于另两个石油股票基金。"
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       "                \"2017-08-23\",\n",
       "                \"2017-08-24\",\n",
       "                \"2017-08-25\",\n",
       "                \"2017-08-29\",\n",
       "                \"2017-08-30\",\n",
       "                \"2017-08-31\",\n",
       "                \"2017-09-01\",\n",
       "                \"2017-09-05\",\n",
       "                \"2017-09-06\",\n",
       "                \"2017-09-07\",\n",
       "                \"2017-09-08\",\n",
       "                \"2017-09-11\",\n",
       "                \"2017-09-12\",\n",
       "                \"2017-09-13\",\n",
       "                \"2017-09-14\",\n",
       "                \"2017-09-15\",\n",
       "                \"2017-09-18\",\n",
       "                \"2017-09-19\",\n",
       "                \"2017-09-20\",\n",
       "                \"2017-09-21\",\n",
       "                \"2017-09-22\",\n",
       "                \"2017-09-25\",\n",
       "                \"2017-09-26\",\n",
       "                \"2017-09-27\",\n",
       "                \"2017-09-28\",\n",
       "                \"2017-09-29\",\n",
       "                \"2017-10-09\",\n",
       "                \"2017-10-10\",\n",
       "                \"2017-10-11\",\n",
       "                \"2017-10-12\",\n",
       "                \"2017-10-13\",\n",
       "                \"2017-10-16\",\n",
       "                \"2017-10-17\",\n",
       "                \"2017-10-18\",\n",
       "                \"2017-10-19\",\n",
       "                \"2017-10-20\",\n",
       "                \"2017-10-23\",\n",
       "                \"2017-10-24\",\n",
       "                \"2017-10-25\",\n",
       "                \"2017-10-26\",\n",
       "                \"2017-10-27\",\n",
       "                \"2017-10-30\",\n",
       "                \"2017-10-31\",\n",
       "                \"2017-11-01\",\n",
       "                \"2017-11-02\",\n",
       "                \"2017-11-03\",\n",
       "                \"2017-11-06\",\n",
       "                \"2017-11-07\",\n",
       "                \"2017-11-08\",\n",
       "                \"2017-11-09\",\n",
       "                \"2017-11-10\",\n",
       "                \"2017-11-13\",\n",
       "                \"2017-11-14\",\n",
       "                \"2017-11-15\",\n",
       "                \"2017-11-16\",\n",
       "                \"2017-11-17\",\n",
       "                \"2017-11-20\",\n",
       "                \"2017-11-21\",\n",
       "                \"2017-11-22\",\n",
       "                \"2017-11-24\",\n",
       "                \"2017-11-27\",\n",
       "                \"2017-11-28\",\n",
       "                \"2017-11-29\",\n",
       "                \"2017-11-30\",\n",
       "                \"2017-12-01\",\n",
       "                \"2017-12-04\",\n",
       "                \"2017-12-05\",\n",
       "                \"2017-12-06\",\n",
       "                \"2017-12-07\",\n",
       "                \"2017-12-08\",\n",
       "                \"2017-12-11\",\n",
       "                \"2017-12-12\",\n",
       "                \"2017-12-13\",\n",
       "                \"2017-12-14\",\n",
       "                \"2017-12-15\",\n",
       "                \"2017-12-18\",\n",
       "                \"2017-12-19\",\n",
       "                \"2017-12-20\",\n",
       "                \"2017-12-21\",\n",
       "                \"2017-12-22\",\n",
       "                \"2017-12-27\",\n",
       "                \"2017-12-28\",\n",
       "                \"2017-12-29\",\n",
       "                \"2018-01-02\",\n",
       "                \"2018-01-03\",\n",
       "                \"2018-01-04\",\n",
       "                \"2018-01-05\",\n",
       "                \"2018-01-08\",\n",
       "                \"2018-01-09\",\n",
       "                \"2018-01-10\",\n",
       "                \"2018-01-11\",\n",
       "                \"2018-01-12\",\n",
       "                \"2018-01-16\",\n",
       "                \"2018-01-17\",\n",
       "                \"2018-01-18\",\n",
       "                \"2018-01-19\",\n",
       "                \"2018-01-22\",\n",
       "                \"2018-01-23\",\n",
       "                \"2018-01-24\",\n",
       "                \"2018-01-25\",\n",
       "                \"2018-01-26\",\n",
       "                \"2018-01-29\",\n",
       "                \"2018-01-30\",\n",
       "                \"2018-01-31\",\n",
       "                \"2018-02-01\",\n",
       "                \"2018-02-02\",\n",
       "                \"2018-02-05\",\n",
       "                \"2018-02-06\",\n",
       "                \"2018-02-07\",\n",
       "                \"2018-02-08\",\n",
       "                \"2018-02-09\",\n",
       "                \"2018-02-12\",\n",
       "                \"2018-02-13\",\n",
       "                \"2018-02-14\",\n",
       "                \"2018-02-22\",\n",
       "                \"2018-02-23\",\n",
       "                \"2018-02-26\",\n",
       "                \"2018-02-27\",\n",
       "                \"2018-02-28\",\n",
       "                \"2018-03-01\",\n",
       "                \"2018-03-02\",\n",
       "                \"2018-03-05\",\n",
       "                \"2018-03-06\",\n",
       "                \"2018-03-07\",\n",
       "                \"2018-03-08\",\n",
       "                \"2018-03-09\",\n",
       "                \"2018-03-12\",\n",
       "                \"2018-03-13\",\n",
       "                \"2018-03-14\",\n",
       "                \"2018-03-15\",\n",
       "                \"2018-03-16\",\n",
       "                \"2018-03-19\",\n",
       "                \"2018-03-20\",\n",
       "                \"2018-03-21\",\n",
       "                \"2018-03-22\",\n",
       "                \"2018-03-23\",\n",
       "                \"2018-03-26\",\n",
       "                \"2018-03-27\",\n",
       "                \"2018-03-28\",\n",
       "                \"2018-03-29\",\n",
       "                \"2018-04-03\",\n",
       "                \"2018-04-04\",\n",
       "                \"2018-04-09\",\n",
       "                \"2018-04-10\",\n",
       "                \"2018-04-11\",\n",
       "                \"2018-04-12\",\n",
       "                \"2018-04-13\",\n",
       "                \"2018-04-16\",\n",
       "                \"2018-04-17\",\n",
       "                \"2018-04-18\",\n",
       "                \"2018-04-19\",\n",
       "                \"2018-04-20\",\n",
       "                \"2018-04-23\",\n",
       "                \"2018-04-24\",\n",
       "                \"2018-04-25\",\n",
       "                \"2018-04-26\",\n",
       "                \"2018-04-27\",\n",
       "                \"2018-05-02\",\n",
       "                \"2018-05-03\",\n",
       "                \"2018-05-04\",\n",
       "                \"2018-05-08\",\n",
       "                \"2018-05-09\",\n",
       "                \"2018-05-10\",\n",
       "                \"2018-05-11\",\n",
       "                \"2018-05-14\",\n",
       "                \"2018-05-15\",\n",
       "                \"2018-05-16\",\n",
       "                \"2018-05-17\",\n",
       "                \"2018-05-18\",\n",
       "                \"2018-05-21\",\n",
       "                \"2018-05-22\",\n",
       "                \"2018-05-23\",\n",
       "                \"2018-05-24\",\n",
       "                \"2018-05-25\",\n",
       "                \"2018-05-29\",\n",
       "                \"2018-05-30\",\n",
       "                \"2018-05-31\",\n",
       "                \"2018-06-01\",\n",
       "                \"2018-06-04\",\n",
       "                \"2018-06-05\",\n",
       "                \"2018-06-06\",\n",
       "                \"2018-06-07\",\n",
       "                \"2018-06-08\",\n",
       "                \"2018-06-11\",\n",
       "                \"2018-06-12\",\n",
       "                \"2018-06-13\",\n",
       "                \"2018-06-14\",\n",
       "                \"2018-06-15\",\n",
       "                \"2018-06-19\",\n",
       "                \"2018-06-20\",\n",
       "                \"2018-06-21\",\n",
       "                \"2018-06-22\",\n",
       "                \"2018-06-25\",\n",
       "                \"2018-06-26\",\n",
       "                \"2018-06-27\",\n",
       "                \"2018-06-28\",\n",
       "                \"2018-06-29\",\n",
       "                \"2018-07-02\",\n",
       "                \"2018-07-03\",\n",
       "                \"2018-07-05\",\n",
       "                \"2018-07-06\",\n",
       "                \"2018-07-09\",\n",
       "                \"2018-07-10\",\n",
       "                \"2018-07-11\",\n",
       "                \"2018-07-12\",\n",
       "                \"2018-07-13\",\n",
       "                \"2018-07-16\",\n",
       "                \"2018-07-17\",\n",
       "                \"2018-07-18\",\n",
       "                \"2018-07-19\",\n",
       "                \"2018-07-20\",\n",
       "                \"2018-07-23\",\n",
       "                \"2018-07-24\",\n",
       "                \"2018-07-25\",\n",
       "                \"2018-07-26\",\n",
       "                \"2018-07-27\",\n",
       "                \"2018-07-30\",\n",
       "                \"2018-07-31\",\n",
       "                \"2018-08-01\",\n",
       "                \"2018-08-02\",\n",
       "                \"2018-08-03\",\n",
       "                \"2018-08-06\",\n",
       "                \"2018-08-07\",\n",
       "                \"2018-08-08\",\n",
       "                \"2018-08-09\",\n",
       "                \"2018-08-10\",\n",
       "                \"2018-08-13\",\n",
       "                \"2018-08-14\",\n",
       "                \"2018-08-15\",\n",
       "                \"2018-08-16\",\n",
       "                \"2018-08-17\",\n",
       "                \"2018-08-20\",\n",
       "                \"2018-08-21\",\n",
       "                \"2018-08-22\",\n",
       "                \"2018-08-23\",\n",
       "                \"2018-08-24\",\n",
       "                \"2018-08-28\",\n",
       "                \"2018-08-29\",\n",
       "                \"2018-08-30\",\n",
       "                \"2018-08-31\",\n",
       "                \"2018-09-04\",\n",
       "                \"2018-09-05\",\n",
       "                \"2018-09-06\",\n",
       "                \"2018-09-07\",\n",
       "                \"2018-09-10\",\n",
       "                \"2018-09-11\",\n",
       "                \"2018-09-12\",\n",
       "                \"2018-09-13\",\n",
       "                \"2018-09-14\",\n",
       "                \"2018-09-17\",\n",
       "                \"2018-09-18\",\n",
       "                \"2018-09-19\",\n",
       "                \"2018-09-20\",\n",
       "                \"2018-09-21\",\n",
       "                \"2018-09-25\",\n",
       "                \"2018-09-26\",\n",
       "                \"2018-09-27\",\n",
       "                \"2018-09-28\",\n",
       "                \"2018-10-08\",\n",
       "                \"2018-10-09\",\n",
       "                \"2018-10-10\",\n",
       "                \"2018-10-11\",\n",
       "                \"2018-10-12\",\n",
       "                \"2018-10-15\",\n",
       "                \"2018-10-16\",\n",
       "                \"2018-10-17\",\n",
       "                \"2018-10-18\",\n",
       "                \"2018-10-19\",\n",
       "                \"2018-10-22\",\n",
       "                \"2018-10-23\",\n",
       "                \"2018-10-24\",\n",
       "                \"2018-10-25\",\n",
       "                \"2018-10-26\",\n",
       "                \"2018-10-29\",\n",
       "                \"2018-10-30\",\n",
       "                \"2018-10-31\",\n",
       "                \"2018-11-01\",\n",
       "                \"2018-11-02\",\n",
       "                \"2018-11-05\",\n",
       "                \"2018-11-06\",\n",
       "                \"2018-11-07\",\n",
       "                \"2018-11-08\",\n",
       "                \"2018-11-09\",\n",
       "                \"2018-11-12\",\n",
       "                \"2018-11-13\",\n",
       "                \"2018-11-14\",\n",
       "                \"2018-11-15\",\n",
       "                \"2018-11-16\",\n",
       "                \"2018-11-19\",\n",
       "                \"2018-11-20\",\n",
       "                \"2018-11-21\",\n",
       "                \"2018-11-23\",\n",
       "                \"2018-11-26\",\n",
       "                \"2018-11-27\",\n",
       "                \"2018-11-28\",\n",
       "                \"2018-11-29\",\n",
       "                \"2018-11-30\",\n",
       "                \"2018-12-03\",\n",
       "                \"2018-12-04\",\n",
       "                \"2018-12-06\",\n",
       "                \"2018-12-07\",\n",
       "                \"2018-12-10\",\n",
       "                \"2018-12-11\",\n",
       "                \"2018-12-12\",\n",
       "                \"2018-12-13\",\n",
       "                \"2018-12-14\",\n",
       "                \"2018-12-17\",\n",
       "                \"2018-12-18\",\n",
       "                \"2018-12-19\",\n",
       "                \"2018-12-20\",\n",
       "                \"2018-12-21\",\n",
       "                \"2018-12-24\",\n",
       "                \"2018-12-27\",\n",
       "                \"2018-12-28\",\n",
       "                \"2019-01-02\",\n",
       "                \"2019-01-03\",\n",
       "                \"2019-01-04\",\n",
       "                \"2019-01-07\",\n",
       "                \"2019-01-08\",\n",
       "                \"2019-01-09\",\n",
       "                \"2019-01-10\",\n",
       "                \"2019-01-11\",\n",
       "                \"2019-01-14\",\n",
       "                \"2019-01-15\",\n",
       "                \"2019-01-16\",\n",
       "                \"2019-01-17\",\n",
       "                \"2019-01-18\",\n",
       "                \"2019-01-22\",\n",
       "                \"2019-01-23\",\n",
       "                \"2019-01-24\",\n",
       "                \"2019-01-25\",\n",
       "                \"2019-01-28\",\n",
       "                \"2019-01-29\",\n",
       "                \"2019-01-30\",\n",
       "                \"2019-01-31\",\n",
       "                \"2019-02-01\",\n",
       "                \"2019-02-11\",\n",
       "                \"2019-02-12\",\n",
       "                \"2019-02-13\",\n",
       "                \"2019-02-14\",\n",
       "                \"2019-02-15\",\n",
       "                \"2019-02-19\",\n",
       "                \"2019-02-20\",\n",
       "                \"2019-02-21\",\n",
       "                \"2019-02-22\",\n",
       "                \"2019-02-25\",\n",
       "                \"2019-02-26\",\n",
       "                \"2019-02-27\",\n",
       "                \"2019-02-28\",\n",
       "                \"2019-03-01\",\n",
       "                \"2019-03-04\",\n",
       "                \"2019-03-05\",\n",
       "                \"2019-03-06\",\n",
       "                \"2019-03-07\",\n",
       "                \"2019-03-08\",\n",
       "                \"2019-03-11\",\n",
       "                \"2019-03-12\",\n",
       "                \"2019-03-13\",\n",
       "                \"2019-03-14\",\n",
       "                \"2019-03-15\",\n",
       "                \"2019-03-18\",\n",
       "                \"2019-03-19\",\n",
       "                \"2019-03-20\",\n",
       "                \"2019-03-21\",\n",
       "                \"2019-03-22\",\n",
       "                \"2019-03-25\",\n",
       "                \"2019-03-26\",\n",
       "                \"2019-03-27\",\n",
       "                \"2019-03-28\",\n",
       "                \"2019-03-29\",\n",
       "                \"2019-04-01\",\n",
       "                \"2019-04-02\",\n",
       "                \"2019-04-03\",\n",
       "                \"2019-04-04\",\n",
       "                \"2019-04-08\",\n",
       "                \"2019-04-09\",\n",
       "                \"2019-04-10\",\n",
       "                \"2019-04-11\",\n",
       "                \"2019-04-12\",\n",
       "                \"2019-04-15\",\n",
       "                \"2019-04-16\",\n",
       "                \"2019-04-17\",\n",
       "                \"2019-04-18\",\n",
       "                \"2019-04-23\",\n",
       "                \"2019-04-24\",\n",
       "                \"2019-04-25\",\n",
       "                \"2019-04-26\",\n",
       "                \"2019-04-29\",\n",
       "                \"2019-04-30\",\n",
       "                \"2019-05-07\",\n",
       "                \"2019-05-08\",\n",
       "                \"2019-05-09\",\n",
       "                \"2019-05-10\",\n",
       "                \"2019-05-13\",\n",
       "                \"2019-05-14\",\n",
       "                \"2019-05-15\",\n",
       "                \"2019-05-16\",\n",
       "                \"2019-05-17\",\n",
       "                \"2019-05-20\",\n",
       "                \"2019-05-21\",\n",
       "                \"2019-05-22\",\n",
       "                \"2019-05-23\",\n",
       "                \"2019-05-24\",\n",
       "                \"2019-05-28\",\n",
       "                \"2019-05-29\",\n",
       "                \"2019-05-30\",\n",
       "                \"2019-05-31\",\n",
       "                \"2019-06-03\",\n",
       "                \"2019-06-04\",\n",
       "                \"2019-06-05\",\n",
       "                \"2019-06-06\",\n",
       "                \"2019-06-10\",\n",
       "                \"2019-06-11\",\n",
       "                \"2019-06-12\",\n",
       "                \"2019-06-13\",\n",
       "                \"2019-06-14\",\n",
       "                \"2019-06-17\",\n",
       "                \"2019-06-18\",\n",
       "                \"2019-06-19\",\n",
       "                \"2019-06-20\",\n",
       "                \"2019-06-21\",\n",
       "                \"2019-06-24\",\n",
       "                \"2019-06-25\",\n",
       "                \"2019-06-26\",\n",
       "                \"2019-06-27\",\n",
       "                \"2019-06-28\",\n",
       "                \"2019-07-01\",\n",
       "                \"2019-07-02\",\n",
       "                \"2019-07-03\",\n",
       "                \"2019-07-05\",\n",
       "                \"2019-07-08\",\n",
       "                \"2019-07-09\",\n",
       "                \"2019-07-10\",\n",
       "                \"2019-07-11\",\n",
       "                \"2019-07-12\",\n",
       "                \"2019-07-16\",\n",
       "                \"2019-07-17\",\n",
       "                \"2019-07-18\",\n",
       "                \"2019-07-19\",\n",
       "                \"2019-07-22\",\n",
       "                \"2019-07-23\",\n",
       "                \"2019-07-24\",\n",
       "                \"2019-07-25\",\n",
       "                \"2019-07-26\",\n",
       "                \"2019-07-29\",\n",
       "                \"2019-07-30\",\n",
       "                \"2019-07-31\",\n",
       "                \"2019-08-01\",\n",
       "                \"2019-08-02\",\n",
       "                \"2019-08-05\",\n",
       "                \"2019-08-06\",\n",
       "                \"2019-08-07\",\n",
       "                \"2019-08-08\",\n",
       "                \"2019-08-09\",\n",
       "                \"2019-08-13\",\n",
       "                \"2019-08-14\",\n",
       "                \"2019-08-15\",\n",
       "                \"2019-08-16\",\n",
       "                \"2019-08-19\",\n",
       "                \"2019-08-20\",\n",
       "                \"2019-08-21\",\n",
       "                \"2019-08-22\"\n",
       "            ]\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_73fe459c8d11419a8906220314fba223.setOption(option_73fe459c8d11419a8906220314fba223);\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x107c3b4a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oilcomp.v_netvalue()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到三个追踪原油期货的基金，走势几乎完全一致，在两年多的时间中产生的误差很有限。\n",
    "奇怪的是，在这段区间石油价格有所回升，同时美股一直运行在牛市，但主力在美股的石油企业的股票走势还比不上原油价格本身，尤其是华宝油气表现极差。也就是说，美股里这些企业估值在不断走低，对应了大家的夕阳产业预期。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 华宝油气：0.15+1.28， 标普石油天然气上游股票指数\n",
    "* 华安石油： 0.12+1.28， 标普全球石油指数\n",
    "* 广发石油： 0.39 亿，规模低于清盘线，请谨慎申购， 0.12+1.3， 道琼斯美国石油开发与生产指数\n",
    "\n",
    "* 易方达原油： 0.12+1.25，标普高盛原油商品指数\n",
    "* 南方原油： 0.12+1.28， 60%WTI原油价格收益率 + 40%BRENT原油价格收益率\n",
    "* 嘉实原油： 0.12+1.28，100%WTI原油价格收益率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "华宝标普油气上游股票 0.31330039728004055\n",
      "广发道琼斯石油指数人民币A 0.23449795919975377\n",
      "华安标普全球石油指数 0.1684688810075437\n",
      "南方原油A 0.23465224682648175\n",
      "易方达原油A类人民币 0.2244510474007535\n",
      "嘉实原油(QDII-LOF) 0.22860537822326174\n"
     ]
    }
   ],
   "source": [
    "for fund in oilcomp.fundobjs:\n",
    "    print(fund.name, fund.algorithm_volatility())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大多数人投资石油基金并不是为了长期价值投资，而是为了做波段，因此波动率越高越好。由此可以看出，华宝油气是波动最大的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们现在研究下原油基金的换仓损耗到底有多高，首先爬取过去一年的 WTI 原油期货价格日线数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get(\n",
    "    \"https://cn.investing.com/common/modules/js_instrument_chart/api/data.php?pair_id=8849&pair_id_for_news=8849&chart_type=area&pair_interval=86400&candle_count=120&events=yes&volume_series=yes&period=1-year\",\n",
    "    headers={\n",
    "        \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36\",\n",
    "        \"Referer\": \"https://cn.investing.com/commodities/crude-oil\",\n",
    "        \"Host\": \"cn.investing.com\",\n",
    "        \"X-Requested-With\": \"XMLHttpRequest\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>netvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>68.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-08-27</td>\n",
       "      <td>68.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-28</td>\n",
       "      <td>68.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>69.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>70.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>69.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>69.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>70.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>69.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-09-05</td>\n",
       "      <td>68.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>67.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>67.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-09-10</td>\n",
       "      <td>67.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>69.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-09-12</td>\n",
       "      <td>70.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>68.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>68.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-09-17</td>\n",
       "      <td>68.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-09-18</td>\n",
       "      <td>69.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-09-19</td>\n",
       "      <td>71.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>70.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>70.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>72.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>72.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>71.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>72.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>73.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>75.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>75.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>76.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>59.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2019-07-16</td>\n",
       "      <td>57.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2019-07-17</td>\n",
       "      <td>56.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2019-07-18</td>\n",
       "      <td>55.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>55.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>56.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2019-07-23</td>\n",
       "      <td>56.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2019-07-24</td>\n",
       "      <td>55.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>56.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2019-07-26</td>\n",
       "      <td>56.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2019-07-29</td>\n",
       "      <td>56.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2019-07-30</td>\n",
       "      <td>58.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>58.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>53.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>55.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>54.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2019-08-06</td>\n",
       "      <td>53.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>51.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>52.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>54.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>54.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2019-08-13</td>\n",
       "      <td>57.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>55.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>54.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>54.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>56.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>56.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>55.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>55.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>53.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  netvalue\n",
       "0   2018-08-24     68.72\n",
       "1   2018-08-27     68.87\n",
       "2   2018-08-28     68.53\n",
       "3   2018-08-29     69.51\n",
       "4   2018-08-30     70.25\n",
       "5   2018-08-31     69.80\n",
       "6   2018-09-02     69.92\n",
       "7   2018-09-03     70.12\n",
       "8   2018-09-04     69.87\n",
       "9   2018-09-05     68.72\n",
       "10  2018-09-06     67.77\n",
       "11  2018-09-07     67.75\n",
       "12  2018-09-10     67.54\n",
       "13  2018-09-11     69.25\n",
       "14  2018-09-12     70.37\n",
       "15  2018-09-13     68.59\n",
       "16  2018-09-14     68.99\n",
       "17  2018-09-17     68.91\n",
       "18  2018-09-18     69.85\n",
       "19  2018-09-19     71.12\n",
       "20  2018-09-20     70.80\n",
       "21  2018-09-21     70.78\n",
       "22  2018-09-24     72.08\n",
       "23  2018-09-25     72.28\n",
       "24  2018-09-26     71.57\n",
       "25  2018-09-27     72.12\n",
       "26  2018-09-28     73.25\n",
       "27  2018-10-01     75.30\n",
       "28  2018-10-02     75.23\n",
       "29  2018-10-03     76.41\n",
       "..         ...       ...\n",
       "234 2019-07-15     59.58\n",
       "235 2019-07-16     57.62\n",
       "236 2019-07-17     56.78\n",
       "237 2019-07-18     55.30\n",
       "238 2019-07-19     55.63\n",
       "239 2019-07-22     56.22\n",
       "240 2019-07-23     56.77\n",
       "241 2019-07-24     55.88\n",
       "242 2019-07-25     56.02\n",
       "243 2019-07-26     56.20\n",
       "244 2019-07-29     56.87\n",
       "245 2019-07-30     58.05\n",
       "246 2019-07-31     58.58\n",
       "247 2019-08-01     53.95\n",
       "248 2019-08-02     55.66\n",
       "249 2019-08-05     54.69\n",
       "250 2019-08-06     53.63\n",
       "251 2019-08-07     51.09\n",
       "252 2019-08-08     52.54\n",
       "253 2019-08-09     54.50\n",
       "254 2019-08-12     54.93\n",
       "255 2019-08-13     57.10\n",
       "256 2019-08-14     55.23\n",
       "257 2019-08-15     54.47\n",
       "258 2019-08-16     54.87\n",
       "259 2019-08-19     56.21\n",
       "260 2019-08-20     56.34\n",
       "261 2019-08-21     55.68\n",
       "262 2019-08-22     55.35\n",
       "263 2019-08-23     53.95\n",
       "\n",
       "[264 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_utc = dt.timezone(dt.timedelta(hours=0))\n",
    "date = []\n",
    "netvalue = []\n",
    "for line in r.json()[\"candles\"]:\n",
    "    date.append(dt.datetime.fromtimestamp(line[0] / 1000, tz_utc).replace(tzinfo=None))\n",
    "    netvalue.append(line[1])\n",
    "df = pd.DataFrame(data={\"date\": date, \"netvalue\": netvalue})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们还需要额外爬取人民币美元汇率数据用于比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get(\n",
    "    \"https://cn.investing.com/common/modules/js_instrument_chart/api/data.php?pair_id=2111&pair_id_for_news=2111&chart_type=area&pair_interval=86400&candle_count=120&events=yes&volume_series=yes&period=1-year\",\n",
    "    headers={\n",
    "        \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36\",\n",
    "        \"Referer\": \"https://cn.investing.com/commodities/currencies/usd-cny\",\n",
    "        \"Host\": \"cn.investing.com\",\n",
    "        \"X-Requested-With\": \"XMLHttpRequest\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>netvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>6.8070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-08-27</td>\n",
       "      <td>6.8156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-28</td>\n",
       "      <td>6.8030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>6.8213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>6.8445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>6.8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>6.8228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>6.8442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-09-05</td>\n",
       "      <td>6.8305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>6.8355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>6.8438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-09-10</td>\n",
       "      <td>6.8556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>6.8730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-09-12</td>\n",
       "      <td>6.8606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>6.8448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>6.8690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-09-17</td>\n",
       "      <td>6.8570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-09-18</td>\n",
       "      <td>6.8616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-09-19</td>\n",
       "      <td>6.8483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>6.8468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>6.8571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>6.8571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>6.8665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6.8786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>6.8902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>6.8689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>6.8689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>6.8689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>6.8689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>6.8689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>6.8777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2019-07-16</td>\n",
       "      <td>6.8762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2019-07-17</td>\n",
       "      <td>6.8736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2019-07-18</td>\n",
       "      <td>6.8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>6.8822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>6.8811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2019-07-23</td>\n",
       "      <td>6.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2019-07-24</td>\n",
       "      <td>6.8725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>6.8726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2019-07-26</td>\n",
       "      <td>6.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2019-07-29</td>\n",
       "      <td>6.8934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2019-07-30</td>\n",
       "      <td>6.8845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>6.8841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>6.8986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>6.9405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>7.0508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2019-08-06</td>\n",
       "      <td>7.0264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>7.0602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>7.0451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>7.0624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>7.0582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2019-08-13</td>\n",
       "      <td>7.0435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>7.0244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>7.0340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>7.0428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>7.0507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>7.0605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>7.0632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>7.0836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>7.0960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  netvalue\n",
       "0   2018-08-24    6.8070\n",
       "1   2018-08-27    6.8156\n",
       "2   2018-08-28    6.8030\n",
       "3   2018-08-29    6.8213\n",
       "4   2018-08-30    6.8445\n",
       "5   2018-08-31    6.8315\n",
       "6   2018-09-03    6.8228\n",
       "7   2018-09-04    6.8442\n",
       "8   2018-09-05    6.8305\n",
       "9   2018-09-06    6.8355\n",
       "10  2018-09-07    6.8438\n",
       "11  2018-09-10    6.8556\n",
       "12  2018-09-11    6.8730\n",
       "13  2018-09-12    6.8606\n",
       "14  2018-09-13    6.8448\n",
       "15  2018-09-14    6.8690\n",
       "16  2018-09-17    6.8570\n",
       "17  2018-09-18    6.8616\n",
       "18  2018-09-19    6.8483\n",
       "19  2018-09-20    6.8468\n",
       "20  2018-09-21    6.8571\n",
       "21  2018-09-24    6.8571\n",
       "22  2018-09-25    6.8665\n",
       "23  2018-09-26    6.8786\n",
       "24  2018-09-27    6.8902\n",
       "25  2018-09-28    6.8689\n",
       "26  2018-10-01    6.8689\n",
       "27  2018-10-02    6.8689\n",
       "28  2018-10-03    6.8689\n",
       "29  2018-10-04    6.8689\n",
       "..         ...       ...\n",
       "231 2019-07-15    6.8777\n",
       "232 2019-07-16    6.8762\n",
       "233 2019-07-17    6.8736\n",
       "234 2019-07-18    6.8800\n",
       "235 2019-07-19    6.8822\n",
       "236 2019-07-22    6.8811\n",
       "237 2019-07-23    6.8792\n",
       "238 2019-07-24    6.8725\n",
       "239 2019-07-25    6.8726\n",
       "240 2019-07-26    6.8792\n",
       "241 2019-07-29    6.8934\n",
       "242 2019-07-30    6.8845\n",
       "243 2019-07-31    6.8841\n",
       "244 2019-08-01    6.8986\n",
       "245 2019-08-02    6.9405\n",
       "246 2019-08-05    7.0508\n",
       "247 2019-08-06    7.0264\n",
       "248 2019-08-07    7.0602\n",
       "249 2019-08-08    7.0451\n",
       "250 2019-08-09    7.0624\n",
       "251 2019-08-12    7.0582\n",
       "252 2019-08-13    7.0435\n",
       "253 2019-08-14    7.0244\n",
       "254 2019-08-15    7.0340\n",
       "255 2019-08-16    7.0428\n",
       "256 2019-08-19    7.0507\n",
       "257 2019-08-20    7.0605\n",
       "258 2019-08-21    7.0632\n",
       "259 2019-08-22    7.0836\n",
       "260 2019-08-23    7.0960\n",
       "\n",
       "[261 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_utc = dt.timezone(dt.timedelta(hours=0))\n",
    "date = []\n",
    "netvalue = []\n",
    "for line in r.json()[\"candles\"]:\n",
    "    date.append(dt.datetime.fromtimestamp(line[0] / 1000, tz_utc).replace(tzinfo=None))\n",
    "    netvalue.append(line[1])\n",
    "usdf = pd.DataFrame(data={\"date\": date, \"netvalue\": netvalue})\n",
    "usdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8381712364505961"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-2].netvalue * usdf.iloc[-2].netvalue / df.iloc[0].netvalue / usdf.iloc[\n",
    "    0\n",
    "].netvalue  # 一年真实的油价变化（人民币计价）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8154402005603893"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund = js\n",
    "float(fund.price[fund.price[\"date\"] == \"2019-08-22\"].netvalue) / float(\n",
    "    fund.price[fund.price[\"date\"] == \"2018-08-24\"].netvalue\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一年大约落后跟踪基准 2.7%， 考虑到嘉实原油一年 1.28% 的管理费，大约还会跑输 1.5% 左右。特别是，这一年，美元走强，基金又有一定现金份额来抵消石油价格下降，因此真实跑输大于 1.5%。其中大部分没意外，都是期货换仓的损失。所以粗略估计，每年的持仓额外成本在 1 到 2 个百分点。\n",
    "\n",
    "由于另两个原油基准宣称的基准略有不同，以下列出仅供参考："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8271919660100425"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund = yfd\n",
    "float(fund.price[fund.price[\"date\"] == \"2019-08-22\"].netvalue) / float(\n",
    "    fund.price[fund.price[\"date\"] == \"2018-08-24\"].netvalue\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8347042694868781"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund = nf\n",
    "float(fund.price[fund.price[\"date\"] == \"2019-08-22\"].netvalue) / float(\n",
    "    fund.price[fund.price[\"date\"] == \"2018-08-24\"].netvalue\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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